Lightweight CNN-Based in-Loop Filter for VVC Intra Coding
Hao Zhang, Cheolkon Jung, Yang Liu, Ming Li
Abstract
In versatile video coding (VVC), the in-loop filters suppress compression artifacts while reducing distortion. However, they have a limit of removing complicated compression artifacts due to the hand-crafted design. In this paper, we propose a convolutional neural network (CNN)-based in-loop filter for VVC intra coding. We introduce depthwise separable convolution and attention mechanism to make the proposed network lightweight and efficient. Moreover, we present two basic modules of residual attention block (RAB) and weakly connected attention block (WCAB) to extract and refine features. Besides, we design a multi-stage training strategy based on progressive learning to maximize the learning ability of the proposed network. Compared with VTM-11.0_NNVC-2.0 anchor, the proposed in-loop filter achieves average -7.08% (Y), -12.46% (U), -12.75% (V) BD-rate gains under All Intra (AI) configuration.